I know for sure that human behavior could be predicted with data science and machine learning. Taking a look at human behavior from a sales data analysis perspective, we can get more valuable insights than from social surveys. In this article, I want to show how machine learning approaches can help with customer demand forecasting. Since I have experience in building forecasting models for retail field products, I'll use a retail business as an example. Moreover, considering uncertainties related to the COVID-19 pandemic, I'll also describe how to enhance forecasting accuracy.

What is the top pain point for business executives? Gartner, the world's largest IT research firm, gives a clear answer: demand volatility. Too many factors from weather fluctuations to posts by social media influencers -- impact buyers, causing them to frequently change their minds. Worse still, things reshaping customer intentions happen quite unexpectedly. Think, for instance, of the teenage climate activist Greta Thunberg.

We're excited to announce that you can now measure the accuracy of your forecasting model to optimize the trade-offs between under-forecasting and over-forecasting costs, giving you flexibility in experimentation. Costs associated with under-forecasting and over-forecasting differ. Generally, over-forecasting leads to high inventory carrying costs and waste, whereas under-forecasting leads to stock-outs, unmet demand, and missed revenue opportunities. Amazon Forecast allows you to optimize these costs for your business objective by providing an average forecast as well as a distribution of forecasts that captures variability of demand from a minimum to maximum value. With this launch, Forecast now provides accuracy metrics for multiple distribution points when training a model, allowing you to quickly optimize for under-forecasting and over-forecasting without the need to manually calculate metrics.

Weather can cause significant fluctuations in consumer demand, and because of the bullwhip effect, it can produce unnecessarily high fluctuations on the supply side as well. And these types of variations typically turn into costs.

Hierarchical time series demands exist in many industries and are often associated with the product, time frame, or geographic aggregations. Traditionally, these hierarchies have been forecasted using top-down, bottom-up, or middle-out approaches. The question we aim to answer is how to utilize child-level forecasts to improve parent-level forecasts in a hierarchical supply chain. Improved forecasts can be used to considerably reduce logistics costs, especially in e-commerce. We propose a novel multi-phase hierarchical (MPH) approach. Our method involves forecasting each series in the hierarchy independently using machine learning models, then combining all forecasts to allow a second phase model estimation at the parent level. Sales data from MonarchFx Inc. (a logistics solutions provider) is used to evaluate our approach and compare it to bottom-up and top-down methods. Our results demonstrate an 82-90% improvement in forecast accuracy using the proposed approach. Using the proposed method, supply chain planners can derive more accurate forecasting models to exploit the benefit of multivariate data.